Data Science Fails: Ignoring Business Rules & Expertise

Nowadays, we have unprecedented access to data, plus the computing power and advanced algorithms to find correlations. We look at a cautionary case study of a cancer center that embarked on an ambitious plan to use AI to eradicate cancer. When AI is being asked to make decisions with significant consequences, such as life and death healthcare recommendations, it needs to be trustworthy. But if you don’t follow best practices, if you don’t include the knowledge of subject matter experts, and if you don’t enforce business rules, your AI project will not be successful.

In this podcast produced by Data Science Central, you’ll learn four AI governance practices that can help you achieve AI success.

Speaker

Colin Priest

VP, AI Strategy, DataRobot

DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!

Omair Tariq
Data Analyst, Symphony Post Acute Network
I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.

Oliver Rees
General Manager – Torque Data at Virgin Australia
At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.

Akshay Tandon
VP of Strategy Analytics, LendingTree
We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.

Evariant